The analysis within this report is intended to inform and better equip the marketing manager of an alcohol distributor, operating in Iowa. An analysis of per capita sales across regions of Iowa will demonstrate where consumption of alcohol is highest, differentiated between different alcohol categories. This will allow the marketing manager to better allocate marketing material to regions where they can be most effective in increasing sales.
The data analyzed in this report contains sales data from 2012 to 2016, measuring both dollar sales and volume sales, and demographic data for all regions of Iowa. (General ACS and Sales data analysis and visualization here)
General Sales analysis of County Level Across Categories
General Sales analysis of City Level Across Categories
Across City and County level, the sales data projected similar pattern as Whisky, Vodka and Rum dominated the most part of the sales, followed by Misc, Tequila, Brandy, Schnapps, Gin, etc. The rankings acroos the top 10 counties with most sales in dollars and most sales in volume stayed constant, with Polk, Linn and Scott county taking the first three across dollar sale and volume sale. City sales saw identical ranking in popularity of product categories. However, there is change of ranking between sale in dollar and sale in volume. Cities including Waterloo, Iowa city and West Des Moines swtiched places, the reason of this swtich in places could due to the fact that Vodka’s retail prices are lower but sold in greater volume, hence causing the change in ranking, besides this, the pattern of the city level analysis stayed constant with the county level.
library(dplyr) library(tidyr)
merged_data_counties <- read.csv(“merged_counties.csv”)
grouped_data_counties <- merged_data_counties %>%
group_by(county, category)
summarized_data <- grouped_data_counties %>%
summarize(total_population = sum(population),
total_sale_volume = sum(sale.volume))
per_capita_data <- summarized_data %>%
mutate(per_capita_consumption = total_sale_volume / total_population)
counties_ranked_data <- per_capita_data %>%
group_by(category, county) %>%
summarize(avg_per_capita_consumption = mean(per_capita_consumption)) %>%
arrange(category, desc(avg_per_capita_consumption)) %>%
group_by(category) %>%
slice(1:10)
counties_ranked_data
Top 10 Counties per capita consumption
merged_data_cities <- read.csv(“merged_cities.csv”)
grouped_data_cities <- merged_data_cities %>%
group_by(city, category)
summarized_data <- grouped_data_cities %>%
summarize(total_population = sum(population),
total_sale_volume = sum(sale.volume))
per_capita_data <- summarized_data %>%
mutate(per_capita_consumption = total_sale_volume / total_population)
cities_ranked_data <- per_capita_data %>%
group_by(category, city) %>%
summarize(avg_per_capita_consumption = mean(per_capita_consumption)) %>%
arrange(category, desc(avg_per_capita_consumption)) %>%
group_by(category) %>%
slice(1:10)
cities_ranked_data
Top 10 Cities per capita consumption
merged_data_zipcodes <- read.csv(“merged_zipcodes.csv”)
grouped_data_zipcodes <- merged_data_zipcodes %>%
group_by(zipcode, category)
summarized_data <- grouped_data_zipcodes %>%
summarize(total_population = sum(population),
total_sale_volume = sum(sale.volume))
per_capita_data <- summarized_data %>%
mutate(per_capita_consumption = total_sale_volume / total_population)
zipcodes_ranked_data <- per_capita_data %>%
group_by(category, zipcode) %>%
summarize(avg_per_capita_consumption = mean(per_capita_consumption)) %>%
arrange(category, desc(avg_per_capita_consumption)) %>%
group_by(category) %>%
slice(1:10)
zipcodes_ranked_data
Top 10 Zipcodes per capita consumption
Top 10 Cities per capita
Top 10 Counties per capita
Top 10 Zipcodes per capita
When examining the top cities for every alcohol category, a particular city stood out for sales of distilled spirits. In Mount Vernon, there is a much higher sale of distilled spirits per capita than in any other city in the state by some margin. This is demonstrated in the screenshot below:
Mount Vernon - An outlier for Distilled Spirits
(in the works)
(in the works)